Hands-on Exercise 03

Author

Ng Meng Ye

Programming Interactive Data Visualisation with R

Getting Started

Importing Data

Interactive Data Visualization - ggiraph

  • tooltip : tooltips to be displayed when mouse is over elements.
  • onclick : JavaScript function to be executed when elements are clicked.
  • data_id : id to be associated with elements (used for hover and click actions)

Tooltip effect with tooltip aesthetic

#| echo: false
#| message: false
#| warning: false
p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = ID),
    stackgroups = TRUE, 
    binwidth = 1, 
    method = "histodot") +
  scale_y_continuous(NULL, 
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 6,
  height_svg = 6*0.618
)

Displaying multiple information on tooltip

The first three lines of codes in the code chunk create a new field called tooltip. At the same time, it populates text in ID and CLASS fields into the newly created field. Next, this newly created field is used as tooltip field as shown in the code of line 7.

exam_data$tooltip <- c(paste0(     
  "Name = ", exam_data$ID,         
  "\n Class = ", exam_data$CLASS)) 

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = exam_data$tooltip), 
    stackgroups = TRUE,
    binwidth = 1,
    method = "histodot") +
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 8,
  height_svg = 8*0.618
)
exam_data$tooltip <- c(paste0(     
  "Name = ", exam_data$ID,         
  "\n Class = ", exam_data$CLASS)) 

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = exam_data$tooltip), 
    stackgroups = TRUE,
    binwidth = 1,
    method = "histodot") +
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 8,
  height_svg = 8*0.618
)

Interactivity

Customising Tooltip style

tooltip_css <- "background-color:black; #<<
font-style:bold; color:white;" #<<

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = ID),                   
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(    #<<
    opts_tooltip(    #<<
      css = tooltip_css)) #<<
)                                        
tooltip_css <- "background-color:white; #<<
font-style:bold; color:black;" #<<

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = ID),                   
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(    #<<
    opts_tooltip(    #<<
      css = tooltip_css)) #<<
)                                        

Displaying statistics on tooltip

tooltip <- function(y, ymax, accuracy = .01) {
  mean <- scales::number(y, accuracy = accuracy)
  sem <- scales::number(ymax - y, accuracy = accuracy)
  paste("Mean maths scores:", mean, "+/-", sem)
}

gg_point <- ggplot(data=exam_data, 
                   aes(x = RACE),
) +
  stat_summary(aes(y = MATHS, 
                   tooltip = after_stat(  
                     tooltip(y, ymax))),  
    fun.data = "mean_se", 
    geom = GeomInteractiveCol,  
    fill = "light blue"
  ) +
  stat_summary(aes(y = MATHS),
    fun.data = mean_se,
    geom = "errorbar", width = 0.2, size = 0.2
  )
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
girafe(ggobj = gg_point,
       width_svg = 8,
       height_svg = 8*0.618)
tooltip <- function(y, ymax, accuracy = .01) {
  mean <- scales::number(y, accuracy = accuracy)
  sem <- scales::number(ymax - y, accuracy = accuracy)
  paste("Mean maths scores:", mean, "+/-", sem)
}

gg_point <- ggplot(data=exam_data, 
                   aes(x = RACE),
) +
  stat_summary(aes(y = MATHS, 
                   tooltip = after_stat(  
                     tooltip(y, ymax))),  
    fun.data = "mean_se", 
    geom = GeomInteractiveCol,  
    fill = "light blue"
  ) +
  stat_summary(aes(y = MATHS),
    fun.data = mean_se,
    geom = "errorbar", width = 0.2, size = 0.2
  )

girafe(ggobj = gg_point,
       width_svg = 8,
       height_svg = 8*0.618)

Hover effect with data_id aesthetic

Interactivity: Elements associated with a data_id (i.e CLASS) will be highlighted upon mouse over. ::: panel-tabset ### The plot

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(           
    aes(data_id = CLASS),             
    stackgroups = TRUE,               
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618                      
)                                        

The code

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(           
    aes(data_id = CLASS),             
    stackgroups = TRUE,               
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618                      
)                                        

:::

Sytling hover effect

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  )                                        
)                                        
p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  )                                        
)                                        

Combining tooltip and hover effect

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = CLASS, 
        data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  )                                        
)                                        
p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = CLASS, 
        data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  )                                        
)   

Click effect with onclick

exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
as.character(exam_data$ID))

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(onclick = onclick),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618)                                        
exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
as.character(exam_data$ID))

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(onclick = onclick),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618)                                        
Warning

Note that click actions must be a string column in the dataset containing valid javascript instructions.

Coordinated Multiple Views with ggiraph

p1 <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +  
  coord_cartesian(xlim=c(0,100)) + 
  scale_y_continuous(NULL,               
                     breaks = NULL)

p2 <- ggplot(data=exam_data, 
       aes(x = ENGLISH)) +
  geom_dotplot_interactive(              
    aes(data_id = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") + 
  coord_cartesian(xlim=c(0,100)) + 
  scale_y_continuous(NULL,               
                     breaks = NULL)

girafe(code = print(p1 + p2), 
       width_svg = 6,
       height_svg = 3,
       options = list(
         opts_hover(css = "fill: #202020;"),
         opts_hover_inv(css = "opacity:0.2;")
         )
       ) 
p1 <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +  
  coord_cartesian(xlim=c(0,100)) + 
  scale_y_continuous(NULL,               
                     breaks = NULL)

p2 <- ggplot(data=exam_data, 
       aes(x = ENGLISH)) +
  geom_dotplot_interactive(              
    aes(data_id = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") + 
  coord_cartesian(xlim=c(0,100)) + 
  scale_y_continuous(NULL,               
                     breaks = NULL)

girafe(code = print(p1 + p2), 
       width_svg = 6,
       height_svg = 3,
       options = list(
         opts_hover(css = "fill: #202020;"),
         opts_hover_inv(css = "opacity:0.2;")
         )
       ) 

Interactive Data Visualization - plotly methods!

Creating an interactive scatter plit: plot_ly()method

plot_ly(data = exam_data, 
             x = ~MATHS, 
             y = ~ENGLISH)

Working with visual variable: plot_ly()method

plot_ly(data = exam_data, 
        x = ~ENGLISH, 
        y = ~MATHS, 
        color = ~RACE)

Creating an interactive scatter plot: ggplotly() method

p <- ggplot(data=exam_data, 
            aes(x = MATHS,
                y = ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))
ggplotly(p)

Coordinated Multiple Views with plotly

d <- highlight_key(exam_data)
p1 <- ggplot(data=d, 
            aes(x = MATHS,
                y = ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

p2 <- ggplot(data=d, 
            aes(x = MATHS,
                y = SCIENCE)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))
subplot(ggplotly(p1),
        ggplotly(p2))

Interactive Data Visualisation - crosstalk methods!

Interactive Data Table: DT package

DT::datatable(exam_data, class= "compact")

Linked brushing: crosstalk method

d <- highlight_key(exam_data) 
p <- ggplot(d, 
            aes(ENGLISH, 
                MATHS)) + 
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

gg <- highlight(ggplotly(p),        
                "plotly_selected")  

crosstalk::bscols(gg,               
                  DT::datatable(d), 
                  widths = 5)        

Programming Animated Statistical Graphics with R

Overview

  1. Create animated data visualization by using gganimate and plotly r packages
  2. Reshape data using tidyr package
  3. Process, wrangle and transform data by using dplyr package

Basic concepts of animation

  1. Many individual plots are built and thens stitched together as movie frames, just like an old-school flip book or cartoon.

Terminology

  1. Frame: Each frame represents a different point in time or a different category
  2. Animation attributes: The settings that control how the animation behaves
Tip

Before you start making animated graphs, you should first ask yourself: Does it makes sense to go through the effort?

Getting Started

Load the R packages

Importing the data

mutate_at change simple text into factors, used for categorical data with a fixed set of possible values, know as Levels.

Animated Data Visualization: gganimate methods

  • transition_*() defines how the data should be spread out and how it relates to itself across time
  • view_*() defines how the positional scales should change along the animation
  • shadow_*() defines how data from other points in times should be presented in the given point in time
  • enter_*()/exit_*() defines how new data should appear and how old data should disappear during the course of animation
  • ease_aes() defines how different aesthetics should be eased during transitions

Building a static population bubble plot

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') 

Building the animated bubble plot

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') +
  transition_time(Year) +       
  ease_aes('linear')          

Animated Data Visualization: plotly

Building an animated bubble plot: ggplot() method

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young')

ggplotly(gg)

Notice that although show.legend = FALSE argument was used, the legend still appears on the plot. To overcome this problem, theme(legend.position='none') should be used as shown in the plot and code chunk below.

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young') + 
  theme(legend.position='none')

ggplotly(gg)

Building an animated bubble plot: plot_ly() method

bp <- globalPop %>%
  plot_ly(x = ~Old, 
          y = ~Young, 
          size = ~Population, 
          color = ~Continent,
          sizes = c(2, 100),
          frame = ~Year, 
          text = ~Country, 
          hoverinfo = "text",
          type = 'scatter',
          mode = 'markers'
          ) %>%
  layout(showlegend = FALSE)
bp